Artificial Societies and GPU-Based Cloud Computing for Intelligent Transportation Management

It is challenging to establish accurate mathematical models for complex systems, and experiments on them are generally costly or even impossible, making it difficult to analyze, control, and manage them. The ACP approach provides a way to attack this difficulty. However, with the agent technologies for the A (artificial societies) part, the burdens from computing agent behaviors and the algorithms' evaluating process are usually very heavy. Fortunately, computing hardware is going through a revolution with the development of graphics processing units (GPUs). A single GPU can provide numerous threads running together and is suitable for parallel computing. This article focuses on the C (computational experiments) part of the ACP approach. It explains the advantages of cloud computing and GPUs and presents the architectures of the GPU-based cloud computing for the transportation systems.

Authors

• Kai Wang
• Zhen Shen

Keywords

• Graphics processing unit
• Cloud computing
• Instruction sets
• Computational modeling
• Mathematical model
• Land transportation
• Intelligent transportation systems
• Intelligent Transportation
• Transportation Management
• Artificial Societies
• Complex Systems
• Transport System
• End-users
• Decision Support
• Parallelization
• Simulated Annealing
• Parallel Algorithm
• Elements Of Society
• Mobile Phone
• Computational Resources
• Geographic Information System
• Pedestrian
• Personal Computer
• Intel Xeon
• Traffic Simulation
• Traffic System
• Road Network
• Lattice Boltzmann Method
• Multi-agent Systems
• Cloud Providers
• Travel Behaviour
• Social Forces
• Daily Plan
• intelligent systems
• GPU
• NVIDIA
• artificial transportation systems